Microsoft, Machine Learning, And "Data Wrangling": ML Leverages Business Intelligence For B2B

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"Data wrangling" was an interesting phrase to hear in the machine learning (ML) presentations at Microsoft Ignite. Interesting because data wrangling is from business intelligence (BI), not from artificial intelligence (AI). Microsoft understands ML incorporates concepts from both disciplines. Further discussions point to another key point: Microsoft understands that business-to-business (B2B) is just as fertile for ML as business-to-consumer (B2C). ML applications with the most press are voice, augmented reality and autonomous vehicles.


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The real big-data problem and why only machine learning can fix it - SiliconANGLE

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Why do so many companies still struggle to build a smooth-running pipeline from data to insights? They invest in heavily hyped machine-learning algorithms to analyze data and make business predictions. Then, inevitably, they realize that algorithms aren't magic; if they're fed junk data, their insights won't be stellar. So they employ data scientists that spend 90% of their time washing and folding in a data-cleaning laundromat, leaving just 10% of their time to do the job for which they were hired. What is flawed about this process is that companies only get excited about machine learning for end-of-the-line algorithms; they should apply machine learning just as liberally in the early cleansing stages instead of relying on people to grapple with gargantuan data sets, according to Andy Palmer, co-founder and chief executive officer of Tamr Inc., which helps organizations use machine learning to unify their data silos.


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